diff --git a/plots/dendrogram-basic/implementations/matplotlib.py b/plots/dendrogram-basic/implementations/matplotlib.py
index b9c240341c..9bfb284383 100644
--- a/plots/dendrogram-basic/implementations/matplotlib.py
+++ b/plots/dendrogram-basic/implementations/matplotlib.py
@@ -1,21 +1,19 @@
 """ pyplots.ai
 dendrogram-basic: Basic Dendrogram
-Library: matplotlib 3.10.8 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-23
+Library: matplotlib 3.10.8 | Python 3.14.3
+Quality: 92/100 | Updated: 2026-04-05
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
-from scipy.cluster.hierarchy import dendrogram, linkage
+from matplotlib.collections import LineCollection
+from scipy.cluster.hierarchy import dendrogram, linkage, set_link_color_palette
 
 
 # Data - Iris flower measurements (4 features for 15 samples)
 np.random.seed(42)
 
-# Simulate iris-like measurements: sepal length, sepal width, petal length, petal width
-# Three species with distinct characteristics
 samples_per_species = 5
-
 labels = []
 data = []
 
@@ -24,10 +22,10 @@
     labels.append(f"Setosa-{i + 1}")
     data.append(
         [
-            5.0 + np.random.randn() * 0.3,  # sepal length
-            3.4 + np.random.randn() * 0.3,  # sepal width
-            1.5 + np.random.randn() * 0.2,  # petal length
-            0.3 + np.random.randn() * 0.1,  # petal width
+            5.0 + np.random.randn() * 0.3,
+            3.4 + np.random.randn() * 0.3,
+            1.5 + np.random.randn() * 0.2,
+            0.3 + np.random.randn() * 0.1,
         ]
     )
 
@@ -36,10 +34,10 @@
     labels.append(f"Versicolor-{i + 1}")
     data.append(
         [
-            5.9 + np.random.randn() * 0.4,  # sepal length
-            2.8 + np.random.randn() * 0.3,  # sepal width
-            4.3 + np.random.randn() * 0.4,  # petal length
-            1.3 + np.random.randn() * 0.2,  # petal width
+            5.9 + np.random.randn() * 0.4,
+            2.8 + np.random.randn() * 0.3,
+            4.3 + np.random.randn() * 0.4,
+            1.3 + np.random.randn() * 0.2,
         ]
     )
 
@@ -48,10 +46,10 @@
     labels.append(f"Virginica-{i + 1}")
     data.append(
         [
-            6.6 + np.random.randn() * 0.5,  # sepal length
-            3.0 + np.random.randn() * 0.3,  # sepal width
-            5.5 + np.random.randn() * 0.5,  # petal length
-            2.0 + np.random.randn() * 0.3,  # petal width
+            6.6 + np.random.randn() * 0.5,
+            3.0 + np.random.randn() * 0.3,
+            5.5 + np.random.randn() * 0.5,
+            2.0 + np.random.randn() * 0.3,
         ]
     )
 
@@ -61,33 +59,57 @@
 linkage_matrix = linkage(data, method="ward")
 
 # Plot
-fig, ax = plt.subplots(figsize=(16, 9))
+fig, ax = plt.subplots(figsize=(16, 9), facecolor="white")
+ax.set_facecolor("#FAFAFA")
+
+# Custom cluster colors via set_link_color_palette (matplotlib/scipy integration)
+cluster_colors = ["#306998", "#D4722A", "#3A8A5C"]
+set_link_color_palette(cluster_colors)
+
+# Set threshold between the 2nd and 3rd highest merge distances to reveal 3 clusters
+sorted_distances = sorted(linkage_matrix[:, 2])
+color_threshold = (sorted_distances[-2] + sorted_distances[-3]) / 2
 
-# Create dendrogram with custom colors
-dendrogram(
+dendro = dendrogram(
     linkage_matrix,
     labels=labels,
     ax=ax,
-    leaf_rotation=45,
-    leaf_font_size=14,
-    above_threshold_color="#306998",  # Python Blue for main branches
-    color_threshold=0.7 * max(linkage_matrix[:, 2]),  # Color threshold for clusters
+    leaf_rotation=40,
+    leaf_font_size=16,
+    above_threshold_color="#AAAAAA",
+    color_threshold=color_threshold,
 )
 
+# Post-render enhancement: adjust line widths via LineCollection traversal
+for child in ax.get_children():
+    if isinstance(child, LineCollection):
+        child.set_linewidths(3.0)
+        child.set_capstyle("round")
+        child.set_joinstyle("round")
+
 # Style
-ax.set_xlabel("Sample", fontsize=20)
-ax.set_ylabel("Distance (Ward)", fontsize=20)
-ax.set_title("dendrogram-basic · matplotlib · pyplots.ai", fontsize=24)
-ax.tick_params(axis="both", labelsize=16)
-ax.tick_params(axis="x", labelsize=14, rotation=45)
+ax.set_xlabel("Iris Sample", fontsize=20, labelpad=10)
+ax.set_ylabel("Ward Linkage Distance", fontsize=20, labelpad=10)
+ax.set_title(
+    "Iris Species Clustering · dendrogram-basic · matplotlib · pyplots.ai",
+    fontsize=24,
+    fontweight="medium",
+    pad=20,
+    color="#333333",
+)
+ax.tick_params(axis="both", labelsize=16, colors="#555555")
+ax.tick_params(axis="x", labelsize=16, rotation=40)
 
-# Adjust spines for cleaner look
 ax.spines["top"].set_visible(False)
 ax.spines["right"].set_visible(False)
+ax.spines["left"].set_linewidth(0.6)
+ax.spines["left"].set_color("#CCCCCC")
+ax.spines["bottom"].set_linewidth(0.6)
+ax.spines["bottom"].set_color("#CCCCCC")
 
-# Add subtle grid on y-axis only
-ax.yaxis.grid(True, alpha=0.3, linestyle="--")
+# Subtle grid on y-axis only
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.6, color="#888888")
 ax.set_axisbelow(True)
 
-plt.tight_layout()
-plt.savefig("plot.png", dpi=300, bbox_inches="tight")
+plt.tight_layout(pad=1.5)
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")
diff --git a/plots/dendrogram-basic/metadata/matplotlib.yaml b/plots/dendrogram-basic/metadata/matplotlib.yaml
index b40e8b8fb6..503760cc92 100644
--- a/plots/dendrogram-basic/metadata/matplotlib.yaml
+++ b/plots/dendrogram-basic/metadata/matplotlib.yaml
@@ -1,163 +1,176 @@
 library: matplotlib
 specification_id: dendrogram-basic
 created: '2025-12-23T10:00:50Z'
-updated: '2025-12-23T10:06:28Z'
-generated_by: claude-opus-4-5-20251101
+updated: '2026-04-05T21:00:26Z'
+generated_by: claude-opus-4-6
 workflow_run: 20457531070
 issue: 0
-python_version: 3.13.11
+python_version: 3.14.3
 library_version: 3.10.8
 preview_url: https://storage.googleapis.com/pyplots-images/plots/dendrogram-basic/matplotlib/plot.png
 preview_html: null
-quality_score: 91
+quality_score: 92
 impl_tags:
   dependencies:
   - scipy
-  techniques: []
+  techniques:
+  - manual-ticks
   patterns:
   - data-generation
+  - explicit-figure
   - iteration-over-groups
   dataprep:
   - hierarchical-clustering
-  styling: []
+  styling:
+  - grid-styling
 review:
   strengths:
-  - Excellent hierarchical clustering visualization with clear species separation
-    (Setosa clearly distinct from Versicolor/Virginica)
-  - Perfect use of scipy.cluster.hierarchy with Ward's method for meaningful distance
-    metric
-  - Color-coded clusters make the hierarchical relationships immediately apparent
-  - Clean, professional styling with removed top/right spines and subtle grid
-  - Realistic iris dataset context that is widely recognized in data science
+  - Excellent visual quality with all font sizes explicitly set and no overlap issues
+  - Professional color palette with colorblind-safe colors and thoughtful use of color
+    threshold to reveal cluster structure
+  - 'Strong visual refinement: removed spines, subtle grid, light background, round
+    line caps create a polished look'
+  - Realistic iris flower data that perfectly demonstrates hierarchical clustering
+    with clear species separation
+  - Clean, linear code structure with good use of matplotlib-specific features like
+    LineCollection traversal
   weaknesses:
-  - Does not leverage any distinctive matplotlib visualization features beyond basic
-    figure/axes setup
-  - Y-axis label could be more descriptive (e.g., Ward Linkage Distance or include
-    clustering interpretation hints)
-  image_description: 'The plot displays a dendrogram (hierarchical clustering tree)
-    with 15 iris flower samples on the x-axis and Ward distance on the y-axis (ranging
-    0-10). The tree structure shows three distinct color-coded clusters: orange branches
-    for Setosa samples (left side), green branches for Versicolor and Virginica samples
-    (center and right). The blue horizontal line at the top connects the Setosa cluster
-    to the Versicolor/Virginica cluster at distance ~10. Sample labels are rotated
-    45 degrees and clearly readable. The title "dendrogram-basic · matplotlib · pyplots.ai"
-    appears at the top. The plot has a clean white background with subtle dashed horizontal
-    grid lines on the y-axis. Top and right spines are removed for a cleaner appearance.'
+  - 'Minor redundancy: tick_params rotation=40 duplicates dendrogram leaf_rotation=40'
+  image_description: 'The plot displays a dendrogram of 15 iris flower samples clustered
+    using Ward''s linkage method. Three distinct species clusters are clearly visible:
+    Setosa (blue, leftmost 5 samples), Versicolor (orange, middle 5 samples), and
+    Virginica (green, rightmost 5 samples). The x-axis is labeled "Iris Sample" with
+    rotated sample names (e.g., Setosa-4, Versicolor-1, Virginica-3). The y-axis shows
+    "Ward Linkage Distance" ranging from 0 to ~10. The title reads "Iris Species Clustering
+    · dendrogram-basic · matplotlib · pyplots.ai" in dark gray. Top and right spines
+    are removed, remaining spines are thin and light gray. A subtle y-axis grid (very
+    low alpha) aids distance reading. Branch lines are thick (3.0) with round caps.
+    Above-threshold merges connecting the three main clusters are rendered in gray.
+    Background is a very light gray (#FAFAFA). The Setosa cluster merges at the highest
+    distance (~10.2), indicating it is the most distinct species.'
   criteria_checklist:
     visual_quality:
-      score: 37
-      max: 40
+      score: 30
+      max: 30
       items:
       - id: VQ-01
         name: Text Legibility
-        score: 10
-        max: 10
+        score: 8
+        max: 8
         passed: true
-        comment: Title at 24pt, axis labels at 20pt, tick labels at 14-16pt, all perfectly
-          readable
+        comment: 'All font sizes explicitly set: title 24pt, axis labels 20pt, tick/leaf
+          labels 16pt'
       - id: VQ-02
         name: No Overlap
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: Sample labels are rotated 45° preventing any overlap, all text fully
-          readable
+        comment: X-axis labels rotated at 40 degrees with adequate spacing, no collisions
       - id: VQ-03
         name: Element Visibility
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: Dendrogram branches are clearly visible with good line weights
+        comment: Branch lines set to 3.0 width with round caps/joins, all structure
+          clearly visible
       - id: VQ-04
         name: Color Accessibility
-        score: 5
-        max: 5
+        score: 4
+        max: 4
         passed: true
-        comment: Orange, green, and blue are colorblind-safe and distinguishable
+        comment: Blue/orange/green palette is colorblind-safe with good luminance
+          contrast
       - id: VQ-05
-        name: Layout Balance
-        score: 5
-        max: 5
+        name: Layout & Canvas
+        score: 4
+        max: 4
         passed: true
-        comment: Good use of 16:9 aspect ratio, well-proportioned margins
+        comment: 16:9 figure with tight_layout, plot fills canvas well with balanced
+          margins
       - id: VQ-06
-        name: Axis Labels
-        score: 1
-        max: 2
-        passed: false
-        comment: '"Sample" and "Distance (Ward)" are descriptive but y-axis could
-          include units'
-      - id: VQ-07
-        name: Grid & Legend
+        name: Axis Labels & Title
         score: 2
         max: 2
         passed: true
-        comment: Subtle dashed grid on y-axis only (alpha=0.3), no legend needed for
-          this plot type
+        comment: 'Descriptive labels: Iris Sample and Ward Linkage Distance'
+    design_excellence:
+      score: 15
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: true
+        comment: Custom color palette, thoughtful typography, professional and cohesive
+          design
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: true
+        comment: Spines removed, thin remaining spines, subtle grid, light background,
+          round line caps
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Color threshold reveals 3 natural clusters, gray above-threshold
+          links de-emphasize high-level merges
     spec_compliance:
-      score: 25
-      max: 25
+      score: 15
+      max: 15
       items:
       - id: SC-01
         name: Plot Type
-        score: 8
-        max: 8
-        passed: true
-        comment: Correct dendrogram/hierarchical clustering visualization
-      - id: SC-02
-        name: Data Mapping
         score: 5
         max: 5
         passed: true
-        comment: Samples on x-axis, merge distances on y-axis as expected
-      - id: SC-03
+        comment: Correct dendrogram visualization using scipy.cluster.hierarchy.dendrogram()
+      - id: SC-02
         name: Required Features
-        score: 5
-        max: 5
+        score: 4
+        max: 4
         passed: true
-        comment: Uses scipy.cluster.hierarchy, shows branch heights proportional to
-          distances, vertical orientation
-      - id: SC-04
-        name: Data Range
+        comment: Ward method, labeled samples, proportional branch heights, scipy
+          hierarchy used
+      - id: SC-03
+        name: Data Mapping
         score: 3
         max: 3
         passed: true
-        comment: All 15 samples visible, full distance range shown
-      - id: SC-05
-        name: Legend Accuracy
-        score: 2
-        max: 2
-        passed: true
-        comment: No legend needed; cluster colors are self-explanatory
-      - id: SC-06
-        name: Title Format
-        score: 2
-        max: 2
+        comment: Samples on x-axis, merge distances on y-axis, all 15 items visible
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
         passed: true
-        comment: Correctly formatted as "dendrogram-basic · matplotlib · pyplots.ai"
+        comment: Title includes dendrogram-basic · matplotlib · pyplots.ai with descriptive
+          prefix
     data_quality:
-      score: 19
-      max: 20
+      score: 15
+      max: 15
       items:
       - id: DQ-01
         name: Feature Coverage
-        score: 8
-        max: 8
+        score: 6
+        max: 6
         passed: true
-        comment: Shows hierarchical structure with clear cluster separations at different
-          distance levels
+        comment: Multiple merge levels, varying distances, clear cluster separation
+          demonstrated
       - id: DQ-02
         name: Realistic Context
-        score: 6
-        max: 7
+        score: 5
+        max: 5
         passed: true
-        comment: Iris flower species is a classic, well-known clustering example
+        comment: Iris flower dataset - classic, real-world scientific scenario
       - id: DQ-03
         name: Appropriate Scale
-        score: 5
-        max: 5
+        score: 4
+        max: 4
         passed: true
-        comment: 15 samples (within recommended 10-50), realistic iris measurements
+        comment: Realistic iris measurements, sensible Ward linkage distances
     code_quality:
       score: 10
       max: 10
@@ -167,41 +180,47 @@ review:
         score: 3
         max: 3
         passed: true
-        comment: 'Clean linear structure: imports → data → linkage → plot → save'
+        comment: 'Linear flow: imports, data, linkage, plot, style, save. No functions
+          or classes'
       - id: CQ-02
         name: Reproducibility
-        score: 3
-        max: 3
+        score: 2
+        max: 2
         passed: true
-        comment: Uses np.random.seed(42)
+        comment: np.random.seed(42) set at top
       - id: CQ-03
         name: Clean Imports
         score: 2
         max: 2
         passed: true
-        comment: Only matplotlib, numpy, and scipy.cluster.hierarchy used
+        comment: 'All imports used: plt, np, LineCollection, dendrogram/linkage/set_link_color_palette'
       - id: CQ-04
-        name: No Deprecated API
-        score: 1
-        max: 1
+        name: Code Elegance
+        score: 2
+        max: 2
         passed: true
-        comment: All APIs are current
+        comment: Clean, well-structured with appropriate use of LineCollection traversal
       - id: CQ-05
-        name: Output Correct
+        name: Output & API
         score: 1
         max: 1
         passed: true
-        comment: Saves as plot.png with dpi=300
-    library_features:
-      score: 0
-      max: 5
+        comment: Saves as plot.png at 300 DPI, no deprecated API
+    library_mastery:
+      score: 7
+      max: 10
       items:
-      - id: LF-01
-        name: Uses distinctive library features
-        score: 0
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 4
         max: 5
-        passed: false
-        comment: Uses scipy's dendrogram function directly; matplotlib is used only
-          for figure setup and styling, not for any distinctive matplotlib-specific
-          visualization features
+        passed: true
+        comment: Axes-level methods, scipy dendrogram with ax parameter, set_link_color_palette
+      - id: LM-02
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: true
+        comment: LineCollection traversal for post-render styling, direct spine/tick
+          manipulation
   verdict: APPROVED
diff --git a/plots/dendrogram-basic/specification.md b/plots/dendrogram-basic/specification.md
index e46394e2c4..36c270aeb6 100644
--- a/plots/dendrogram-basic/specification.md
+++ b/plots/dendrogram-basic/specification.md
@@ -13,8 +13,9 @@ A dendrogram visualizes hierarchical clustering by showing how data points or cl
 
 ## Data
 
+- `features` (numeric matrix) - measurement values for each item (e.g., petal length, sepal width), used to compute distances
 - `labels` (string) - names or identifiers for each item being clustered
-- `linkage_matrix` (numeric) - output from scipy's linkage function containing merge distances
+- `linkage_matrix` (numeric) - output from scipy's linkage function containing merge distances, computed from features
 - Size: 10-50 items recommended for readable dendrograms
 - Example: hierarchical clustering of iris flower species by measurements
 
diff --git a/plots/dendrogram-basic/specification.yaml b/plots/dendrogram-basic/specification.yaml
index d731af3645..b6dae88c3d 100644
--- a/plots/dendrogram-basic/specification.yaml
+++ b/plots/dendrogram-basic/specification.yaml
@@ -6,7 +6,7 @@ title: Basic Dendrogram
 
 # Specification tracking
 created: 2025-12-15T20:43:34Z
-updated: 2025-12-15T20:43:34Z
+updated: 2026-04-05T12:00:00Z
 issue: 986
 suggested: MarkusNeusinger
 
@@ -17,6 +17,7 @@ tags:
     - tree
   data_type:
     - hierarchical
+    - numeric
   domain:
     - statistics
     - science